Explainable AI (XAI) for Ocean Health: Exploring the Role of Explainable AI in Enhancing Ocean Health

Deep learning models have undergone a significant transformation in terms of their understandability and transparency as a result of the introduction of Explainable Artificial Intelligence (XAI). Gaining insight into the internal workings of the XAI model enables researchers to verify its predictions and pinpoint possible areas for enhancement. Additionally, by facilitating more efficient dissemination of research results to a wider range of stakeholders, it increases trust in the accuracy of AI-powered evaluations of ocean health. In essence, the application of XAI ultimately improves the accountability and interpretability of AI models, making them more useful instruments for ocean monitoring researchers. This study investigates the application of Explainable Artificial Intelligence (XAI) in the context of assessing the condition of the ocean’s health. This study highlights the significance of Explainable Artificial Intelligence (XAI) and its capacity to address pertinent challenges. A deep learning-based CNN is utilized to training and test with sensor data for ocean anomaly detection. Results indicated lesser RMSE and greater values of precision which are signs of efficient prediction accuracy of the given model.